Approximate Attention

Approximate attention methods aim to address the computational limitations of standard attention mechanisms, particularly the quadratic complexity with respect to sequence length, hindering the use of transformers in applications involving long sequences or high-resolution images. Current research focuses on developing efficient algorithms, such as those employing low-precision arithmetic, adaptive patching, kernel density estimation, and locality-sensitive hashing, to reduce computational cost while maintaining accuracy. These advancements are crucial for deploying large language models and other transformer-based architectures in resource-constrained environments and for scaling to larger datasets and more complex tasks, impacting both theoretical understanding and practical applications of deep learning.